Scrutinization of Authorial Stance on Artificial Intelligence: A Corpus-Based Study of Communicative Verbs in AI-based Research Article Conclusions

This study examines how public verbs, private verbs and suasive verbs contribute to authorial position in research conclusions that are mostly dependent on artificial intelligence. Using a corpus-based approach, 102 research findings from high-impact AI publications published between 2020 and 2024 were read. The study is centered on the aspects of attitude, engagement and graduation and in the process of doing so, the public verbs, private verbs and suasive verbs are identified with the help of appraisal theory. In particular, the results reveal how authors believe the language applying verbs of judgment and evaluating the impact of AI in the society; whereas, authors engage with counterarguments through verbs of engagement. This work contributes to the understanding of the authorial attitude in this emerging field by enriching a knowledge of how linguistic choices relate to academic written discourse in AI. Their implication implies that paying attention to the impact of private, public and suasive verbs may help researchers and corpus-based research authorial posture or stance, public, private and suasive verbs artificial intelligence (AI), and research findings verbs utilizing the framework of appraisal theory. The findings show a pattern of evaluative language that emphasizes the writers' viewpoints on the ethical issues, limitations, and possibilities of AI. In particular, verbs of judgment and evaluation are commonly employed to convey opinions regarding the impact of AI on society, whereas verbs of engagement show the authors' exchanges with opposing views. This study offers a deeper understanding of authorial attitude in this developing discipline by shedding light on how linguistic choices influence academic discourse in AI. The results have important instructional ramifications, indicating that researchers and students might develop more thoughtful and convincing academic conclusions by being aware of public, private and suasive verbs. Future research could look at the function of evaluative language in interdisciplinary AI studies or broaden the focus to include more linguistic components.